BackgroundThere is great interest in finding ways to identify patients who will develop toxicity to cancer therapies. This has become especially pressing in the era of immune therapy, where toxicity can be long-lasting and life-altering, and primarily comes in the form of immune-related adverse effects (irAEs). Treatment with the first drugs in this class, anti-programmed death 1 (anti-PD1)/programmed death-ligand 1 (PDL1) checkpoint therapies, results in grade 2 or higher irAEs in up to 25%–30% of patients, which occur most commonly within the first 6 months of treatment and can include arthralgias, rash, pruritus, pneumonitis, diarrhea and/or colitis, hepatitis, and endocrinopathies. We tested the hypothesis that germline microRNA pathway functional variants, known to predict altered systemic stress responses to cancer therapies, would predict irAEs in patients across cancer types.MethodsMicroRNA pathway variants were evaluated for an association with grade 2 or higher toxicity using four classifiers on 62 patients with melanoma, and then the panel’s performance was validated on 99 patients with other cancer types. Trained classifiers included classification trees, LASSO-regularized logistic regression, boosted trees, and random forests. Final performance measures were reported on the training set using leave-one-out cross validation and validated on held-out samples. The predicted probability of toxicity was evaluated for its association, if any, with response categories to anti-PD1/PDL1 therapy in the melanoma cohort.ResultsA biomarker panel was identified that predicts toxicity with 80% accuracy (F1=0.76, area under the curve (AUC)=0.82) in the melanoma training cohort and 77.6% accuracy (F1=0.621, AUC=0.778) in the pan-cancer validation cohort. In the melanoma cohort, the predictive probability of toxicity was not associated with response categories to anti-PD1/PDL1 therapy (p=0.70). In the same cohort, the most significant biomarker of toxicity in RAC1, predicting a greater than ninefold increased risk of toxicity (p<0.001), was also not associated with response to anti-PD1/PDL1 therapy (p=0.151).ConclusionsA germline microRNA-based biomarker signature predicts grade 2 and higher irAEs to anti-PD1/PDL1 therapy, regardless of tumor type, in a pan-cancer manner. These findings represent an important step toward personalizing checkpoint therapy, the use of which is growing rapidly.

No abstract

No abstract

96 Background: Treatment with anti-PD1/anti-PDL1 agents is associated with toxicity termed immune related adverse events (iRAEs). While the prevalence of Grade 2 and higher iRAEs is approximately 25-30%, biomarkers have not been previously identified. We tested the hypothesis that functional, germ-line mutations would predict iRAEs. Methods: Four classifiers were trained on a set of 61 melanoma patients evaluated for toxicity and response. Subjects were classified as experiencing high toxicity (≥ Grade 2) vs low toxicity (< Grade 2). Performance of the classifiers was tested on a validation set of 89 cancer patients with a variety of cancer types, with the most common being GU and NSCLC. Classifiers were built for each treatment of marker data including classification trees, LASSO-regularized logistic regression, boosted trees (BT), and random forests. The final performance measures, accuracy, specificity, sensitivity, negative predictive value, positive predictive value, area under the curve (AUC), and F1 score, were reported on the categorical treatment of the training data using leave-one-out cross validation on the validation data. We also evaluated the association between our most significant toxicity biomarker and response to anti-PD1/PDL1 therapy. Results: Within the melanoma training sample, we found a biomarker signature where toxicity is predicted with 79.0% accuracy (F1 = .714, AUC = .827) using BT. The same biomarker panel also accurately predicted toxicity in the validation cohort with 85.6% accuracy (F1 = .760, AUC = .883). Of the most predictive biomarkers, three were in microRNA binding sites in RAC1, CD274, and KRAS, two in immune related genes IL2RA and FCGR2A, and one in the DNA repair gene ATM. Our most significant biomarker in RAC1 did not predict response to anti-PD1/PDL1 treatment (p=0.91). Conclusions: We have identified a germ-line biomarker signature which predicts Grade 2 or higher iRAEs for patients treated with anti-PD1/anti-PDL1 therapy, regardless of cancer type, and does not predict an increased likelihood of response to these therapies. These findings are an important step in defining how to better safely personalize immune therapy, whose use is growing rapidly.